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Big Data Solutions for
Healthcare
Wayne Wu, Global Health Solution Architect, Intel
Hubert Ding, Healthcare Solution Architect, Intel


BIGS001
2
Agenda

    • Healthcare and Big Data Trends
    • What is Big Data in Healthcare?
    • Big Data Challenges
    • Methods to Manage Big Data
    • Use Cases
    • Summary and Next Steps




    The PDF for this Session presentation is available from our
    Technical Session Catalog at the end of the day at:
           intel.com/go/idfsessionsBJ
    URL is on top of Session Agenda Pages in Pocket Guide
3
Agenda

    • Healthcare and Big Data Trends
    • What is Big Data in Healthcare?
    • Big Data Challenges
    • Methods to Manage Big Data
    • Use Cases
    • Summary and Next Steps




4
We are at an Inflection Point in
    Healthcare - TRENDS



                                           % of population over age 60




                                                                   30+ %




                                                                    25-29%




                                                                    20-24%




                                                                                                                  2050
                                                                   10-19%




                                                                    0-9%


                                                                                 WW Average Age 60+: 21%

                                                                                   Source: United Nations “Population Aging 2002”




    Healthcare costs are                                                      Global AGING                                          U.S. Healthcare BIG DATA
            RISING                                                          Average age 60+:                                                   Value
     Significant % of GDP                                                  growing from 10% to                                       $300 Billion in value/year
                                                                               21% by 2050                                          ~ 0.7% annual productivity
                                                                                                                                                growth




Source: McKinsey Global Institute Analysis
ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast

5
We are at an Inflection Point in
     Healthcare - TRENDS
                 Storage Growth
              Total Data Healthcare Providers (PB)
    15000
                                                      Admin
                                                      Imaging    Medical Imaging Archive Projection
    10000                                                                    Case from just 1 healthcare system
                                                      EMR
                                                      Email
    5000
                                                      File
                                                      Non Clin Img
       0
                                                      Research
            2010 2011 2012 2013 2014 2015




       Data Explosion projected to reach 35 Zetabytes by 2020, with
                       a 44-fold increase from 2009
Source: McKinsey Global Institute Analysis
ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast

6
Agenda

    • Healthcare and Big Data Trends
    • What is Big Data in Healthcare?
    • Big Data Challenges
    • Methods to Manage Big Data
    • Use Cases
    • Summary and Next Steps




7
Big Data in Healthcare

    Where is the data coming from?
                                            2. Clinical Decision Support &
              1. Pharma/Life Sciences         Trends (includes Diagnostic
                                                         Imaging)
                                             4. Patient Behavior/Social
         3. Claims, Utilization and Fraud
                                                     Networking



    How do we create value? (examples)

              1. Personalized Medicine       2. Clinical Decision Support


                                            4. Analytics for Lifestyle and
           3. Enhanced Fraud Detection
                                              Behavior-induced Diseases




    McKinsey Global Institute Analysis
8
Big Data Solution for Healthcare

       Health Info                             Personal Health
                           Primary Care                           Aging Society
       Services                                 Management


       New Healthcare     Clinical Decision     Personalized
       Applications                                              Cancer Genomics
                               Support            Medicine



       Analytics and                                             Medical Imaging
                          SQL-like Query      Machine Learning
       Visualization                                                Analytics



       Data Processing/
                          Medical Records      Genome Data       Medical Images
       Management



       Distributed           Storage            Security and         Imaging
       Platform            Optimization           Privacy          Acceleration



9
Agenda

     • Healthcare and Big Data Trends
     • What is Big Data in Healthcare?
     • Big Data Challenges
     • Methods to Manage Big Data
     • Use Cases
     • Summary and Next Steps




10
Big Data Challenges are More than Data Size...
     And Require New Technologies


                         Lab results, billing data, images, sensors data from
          Volume         devices, genomics



                         •   Structured data in standard formats like HL7
          Variety        •   Unstructured data from dictations, transcription,
                             photos, images



                         Analyzing data from existing databases for claims,
           Value         patient history, archived images, real-time data
                         analytics for clinical decision support


                         • Realtime rather than batch-style analysis
          Velocity       • Data streamed in, tortured, and discarded
                         • Making impact on the spot rather than after-the-fact



       Traditional business solutions connecting to new data and
           analytics models for real-time value opportunities
11
Agenda

     • Healthcare and Big Data Trends
     • What is Big Data in Healthcare?
     • Big Data Challenges
     • Methods to Manage Big Data
     • Use Cases
     • Summary and Next Steps




12
All Eyes on Data for Value
       Big Data Storage Considerations
                                                Traditional Solution
       Traditional Storage Approaches
                                                   Environments
           Large Analytics – Hadoop*
                                                 ERP, CRM, Batch,
               Large DB – Hive*
                                                     OLTP-DB
            Large Backup – Lustre*




                        Edge Servers

                                                              Analytical
                                                           Synchronization
                                                             End-to-End
                                                         Machine-to-Machine
                                                          Source-to-Source




                                          Data Source
                                    Text-Voice-Video-Sensor
                                       Requesting Or M2M
                                         Communication
                                  Batch – Business Applications



 Rich Visualization – Secure Data Analysis and Caching


13
All Eyes on Data for Value                                                             Data Center Provisioning
                                                                                                     Discrete
                                                                                                      Virtual
                                                                                               Cloud – As A Service
       Big Data Storage Considerations                                                                 HPC
                                                Traditional Solution
       Traditional Storage Approaches
                                                   Environments
           Large Analytics – Hadoop*
                                                 ERP, CRM, Batch,
               Large DB – Hive*
                                                     OLTP-DB
            Large Backup – Lustre*




                        Edge Servers

                                                              Analytical
                                                           Synchronization
                                                             End-to-End
                                                         Machine-to-Machine
                                                          Source-to-Source


                                                                              Operational Solution Stack Example
                                                                                       Applications & Services
                                          Data Source
                                    Text-Voice-Video-Sensor                   Visualization – File Structure & Analytical
                                       Requesting Or M2M                                         Tools
                                         Communication                         Data Delivery, Operational & Graphical
                                  Batch – Business Applications                               Analytics
                                                                                Data Management & Computational
                                                                                           Analytics
 Rich Visualization – Secure Data Analysis and Caching                          Compute – Storage & Infrastructure
                                                                                           Platforms

14
Enterprise Big Data Architecture
     STRUCTURED                                                                                  ENTERPRISE
                                                         DATA PLATFORMS
                                                                      DATA PLATFORMS               TOOLS


       Legacy                              Node   Node   Node                                     ODS & Data
                                                                                                    Marts

                                                                         Data Mining
                                              Hadoop*                                                  Dev IDE
        Logs
                              Create Map




                    CONSUME                   REDUCE            IMPORT
                                                                          Enterprise Data
                                                                            Warehouse
UNSTRUCTURED
                                                                                                    Visualize


     Social & Web                                                            No-SQL
                                                                                                  Spreadsheets
                                                                                            INSIGHTS
       Legacy                                                                                           APPS
      Document                                                           In Memory DB
        Types
                                                                              SQL

                                                                                                        Web
Transcriptions &
                                                                                                        Apps
     Notes
                                                                             RDBMS                     MashUps



15
Big Data Architectural Framework




                                                                                                               Data
                                Provisioning Models-Storage & Connectivity Considerations                     Velocity
                    NAS - SAS and
                                                   Databases          10GBe       MPP Databases                 Data
                     Distributed
                                                 DBMS / NoSQL       Fast Fabric   DW Appliances             Vulnerability
                      Storage


     Human
                                                     Data Sources                                            Security
     Genome                   Surveillance and                  Text, Video                           GIS    Services
                 Diagnostic         Medical                                        Social   Medical
     & Drug      Images
                               Medical Device            Log     and Audio                                    Privacy
                                    Devices
                              Streaming Data                                       Media    Records
     Discovery                                          Files                                               Compliance


                 Provisioning Models Can Vary by Data Characteristics
16
Big Data Architectural Framework




                                                       Data as a Services
                                            Distributed              Virtual          Persistence
     HPC / TCP                                                                                              Vertically
                                           Event, Message Real-Time, Cached, Federated EDW, Marts
       MIC                                                                                                 Integrated        Data
                                                                                                            Software     Characteristics
                                                                                                              Intel
                                      Data ingestion, Integration and Processing Services                     AIM        Data Volume
                                                                                                              Suite          and
                                                    Distributed High Performance      Integration
                                                           Data Processing                                                  Quality
                                                                                         Tools
                                                          Hadoop* MapReduce
                              Cloud
                                                                                                                             Data
                                 Provisioning Models-Storage & Connectivity Considerations                                  Velocity
                    NAS - SAS and
                                                    Databases             10GBe       MPP Databases                          Data
                     Distributed
                                                  DBMS / NoSQL          Fast Fabric   DW Appliances                      Vulnerability
                      Storage


     Human
                                                      Data Sources                                                        Security
     Genome                    Surveillance and                       Text, Video                           GIS           Services
                 Diagnostic          Medical                                            Social   Medical
     & Drug      Images
                                Medical Device            Log          and Audio                                           Privacy
                                     Devices
                               Streaming Data                                           Media    Records
     Discovery                                           Files                                                           Compliance


                 Provisioning Models Can Vary by Data Characteristics
17
Big Data Architectural Framework
                                                                                                           Data Access             Data
                                                                                                               User              Visibility
                                                                                                           Authentication

                                                                                                                                NLP/Semantic
       Custom Analytic Solutions                                              BI & Predictive Analytics                           Search/
                                                     Integrated                                                                   Machine
      MapReduce       Textual Analytics             Analytics with         Existing BI/Analytics                                  Learning
                                                       Hadoop                 with in-database                                   Knowledge
           Streaming Analytics                        Support             data processing support                               Management


                                                            Data as a Services
                                              Distributed              Virtual          Persistence
     HPC / TCP                                                                                                     Vertically
                                             Event, Message Real-Time, Cached, Federated EDW, Marts
       MIC                                                                                                        Integrated        Data
                                                                                                                   Software     Characteristics
                                                                                                                     Intel
                                       Data ingestion, Integration and Processing Services                           AIM            Data
                                                                                                                     Suite         Volume
                                                         Distributed High Performance        Integration
                                                                Data Processing                                                     and
                                                                                                Tools                              Quality
                                                               Hadoop* MapReduce
                               Cloud
                                                                                                                                    Data
                                   Provisioning Models-Storage & Connectivity Considerations                                       Velocity
                     NAS - SAS and
                                                       Databases               10GBe          MPP Databases                          Data
                      Distributed
                                                     DBMS / NoSQL            Fast Fabric      DW Appliances                      Vulnerability
                       Storage


     Human
                                                            Data Sources                                                          Security
     Genome                      Surveillance and                          Text, Video                             GIS            Services
                  Diagnostic           Medical                                                 Social   Medical
     & Drug       Images
                                  Medical Device                Log         and Audio                                              Privacy
                                       Devices
                                 Streaming Data                                                Media    Records
     Discovery                                                 Files                                                             Compliance


                  Provisioning Models Can Vary by Data Characteristics
18
Accessing Big Data (Clients)
      “Know Me”                   “Free Me”             “Express Me”          “Link Me”




                                    Mobile                Laptops,
                          Smart     Clinical   Tablet    Ultrabook™   Fixed    Digital
                          Phone    Assistant    PCs       Devices      PCs    Signage    Kiosk

             Mobility

            Vital sign,
          I & O entry

          Medication
       administration

           Template
          data entry

     Free-format text
           data entry

     Large diagnostic
              images

         Data inquiry


       Manageability


19
Building on the Ecosystem
     Database and Analytics Environments Optimized on Intel

                                                                        Life Sciences
     Database and compute infrastructure   Analytics engines    Workloads & Solutions

     Relational


                           VOLTDB



                                                    EXALYTICS
     Nonrelational



                                                                         Open Source:
                                                                         BLAST, FASTA,
                                                                         ClustalW, HMMER,
                                                                         Darwin, etc.




       No Matter the Choice: All optimized on Intel® Xeon® processor
                              based hardware
20
Intel® Products and Software For Big Data

                                            Scaling
               Compute
                                     Flexible Workloads &
      Intel® Xeon® processor E5-

      and E7 based servers up to            Analysis            Technical Compute
     80% performance boost with           Optimized           Intel Xeon processor E5-
     hardware-enhanced security
                                        Data Delivery &        based servers for TCP
                                                                 Intel® Xeon Phi™ co-
                Storage                  Management                   processor
     Intelligent scale-out storage   Software Ecosystem        Integrated Systems
         built with Intel Xeon
                                        Interconnect        Embedded Analysis Solutions
     processor E5-based storage                                    From Intel ISG
                                          Efficiency
                                       Robust & Secure
                                         Interconnect
                                        Visibly Mobile      Intel Software EcoSystem
       Fast Fabric & Caching          Performance Client              Hadoop*
        Investing in new fabric                                        Lustre*
              approaches               Rich Visualization            In-memory
      non-volatile memory that         Seamless Access        In stream data analysis
     provide capacity caching for                               End to end security
             data velocity
                                                               Performance Client
               Network                                        Rich modeling support
         Intelligent scale-out                              Client – server application
              networking                                           management
        from 10GBe – 40GBe




21
Examples of Intel-powered Servers in
     Big Data and Analytics




     Cisco* UCS Server1                                   Dell* PowerEdge* C Series2                                     Oracle* Sun Fire* server3
     Intel® Xeon®                                         Intel Xeon processor                                           Intel Xeon processor E7-
     processor 5600                                       5500/5600                                                      4800

      Cisco UCS server with EMC                           The Dell | Cloudera* solution                                Oracle Exalytics* In-Memory
      Greenplum MR software -                             for Apache* Hadoop combines                                  Machine, features the Oracle
      “enterprise-class”                                                                                               BI Foundation Suite and
      Hadoop* distribution that                                                                                        Oracle TimesTen In-Memory
      features technology from                                                                                         Database for Exalytics
      MapR
     Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of
     271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New
     score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon®
     processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or
     Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data
     are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.

1 http://gigaom.com/cloud/ciscos-servers-now-tuned-for-hadoop/
2 http://www.businesswire.com/news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data
3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine

22
Big Data Applications in Healthcare (PRC)


                                        2. Clinical
     •药品研发                               Decision     •临床数据比对
     对药品实际 作用进行分析;实   1. Pharma/Life    Support &     匹配同类型的病人,用药
     施药品市场预测              Sciences        Trends
                                                      •临床决策支持
     •基因测序                                            利用规则和数据实时分析给
     •分布式计算加快基因测序计算                     (includes     出智能提示
     效率                                 Diagnostic
                                        Imaging)


                                                      •远程监控
     •公共卫生实时统计分析                                      采集并分析病人随身携带仪
     发现公共卫生疫情及公民健康                       4. Patient   器数据,给出智能建议
     状况                  3. Claims,
                                         Behavior/    •人口统计学分析
     •新农合基金数据分析         Utilization &
                                           Social     对不同群体人群的就医,健
     及时了解基金状况,预测风险          Fraud                     康数据实施人口统计分析
     辅助制定农合基金的起付线,                      Networking
     赔付病种等                                            •了解病人就诊行为
                                                      发现病人的特定就诊行为,
     •基本药物临床应用分析                                      分配医疗资源
     分析基本药物在处方中的比例




23
Agenda

     • Healthcare and Big Data Trends
     • What is Big Data in Healthcare?
     • Big Data Challenges
     • Methods to Manage Big Data
     • Use Cases
     • Summary and Next Steps




24
Use Case: Regional Health Info Network – China
     Real-time Clinical Decision Support

                                                Data Analytic                  Care Coordination
     • Real-time and recursive information      R&D
                                                …
                                                                               Clinical decision support
                                                                               …
       processing of health data (EHR,
       medical images) to support care          RHIN
       coordination, clinical decision          Ancillary           Health       EHR          Registries
       support, and public health                Data &
                                                Services
                                                                 Information
                                                                     DW
                                                                                Data &
                                                                               Services
                                                                                                Data &
                                                                                               Services
       management
     • Enabling health data analytic with
                                                        Longitudinal Record Services
       Hadoop* (HBase*/Hive*)
     • Potential to scale cross geos and                    Health Information Access Layer

       across sectors/segments
                                                                       Primary care        Public
     • Involving local ISVs, local OEMs             Hospital
                                                                       (Grassroots)        Health

     • Technical Challenges
        –   Transforming the relational DB to
            Hadoop (HBase/Hive)
        –   Addressing the usages of big data
            analytics in RHIN



25
RHIN/Grassroots Solution with Big Data (Hadoop*)
       Integrated User Interface(Citizen, Physician, Health Authority)


     Cloud -based Regional Healthcare Service                Distributed Data Service
                     System                                           System
          Multi-Tenancy Application                                    Presentation
                                                                     (Report, Viewer)
      Pubic            Medical
      Health           Service           New Rural
                                                               Data Mining
                                                               (Mahout*)
                                                                                          Language & Compiler
                                                                                                (Hive*)

                                          Medical
                                                           Distributed Batch Processing           Real-time
                                         Insurance                  Framework                     Database
     Operation        Drug Mgt.                                    (MapReduce)                    (HBase*)
       Mgt.            Service
                                                           Distributed File System        Coordination Service
                                                                   (HDFS)                    (ZooKeeper*)

         Infrastructure Virtualization                     Structured Data Collector       Log Date Collector
                                                                   (Sqoop*)                    (Flume*)
          Network        Storage         Server
       Virtualization Virtualization Virtualization
                                                               EHR data Repository


                  Health Information Access Layer (HIAL)

                                                                                          Grassroots
        Hospital                                Hospital                                     Care
26
                                                                                          Institution
Use Case: NEXTBIO
     Analytics for Genomics Data

      •    Cost to sequence a genome has fallen by
           800x in the last 4 years
      •    Each genome has ~4 million variants
      •    Growth in the genomics data in the public
           and private domain
      •    Data available in variety of sources
            –   Structured, semi-structured, unstructured
      •    New aggregated data growing
           exponentially



                             Data             Interpretation &        Commercializing
      Sequencing         Processing               Analytics                 Targeted
                        Cloud Storage                                     Therapeutics
                        Visualization                                      Companion
           3 Billion
          Base Pairs       Millions of         Millions of Variants        Diagnostics
                            Variants           Millions of Patients   Actionable Biomarkers




27
Use Case: NEXTBIO
     Patient Correlation Data




                                                Novel Discoveries
                                                        Biomarkers
                                                   Disease Mechanism
                                                     Drug Indications
                                                 Clinical Trial Parameters
                                                   Patient Care Options




       Large content repository of public and private genomic data
       combined with proprietary and patented correlation engine
28
Use Case: NEXTBIO
     Nextbio & Intel Collaboration

Technical Challenge:
•    Immutable Data – write once,
     never change, read many times
•    Traditional Bloom Filters works
•    Hadoop* & HBase* well suited
      1 genome  10 million rows
      100 genomes  1billion rows
      1M genomes  10 trillion rows
      100M genomes  1 quadrillion
      1,000,000,000,000,000 rows
•    App can dynamically partitions HBase
     as data size grows
Intel Optimizations for Hadoop:
•    Optimized Hadoop stack in open
     source
•    Stabilize HBase to provide reliable
     scalable deployment
•    Optimize and support scale-out as
     data size dramatically grows
•    Exploring cluster auto tuning,
     Security & Compliance, etc.

29
Use Case: Big Data at Kaiser Permanente




30
Data Trends
                 World’s Data                                       Kaiser’s
                                                                     Data 90%
                              80%
     STRUCTURED           UNSTRUCTURED             STRUCTURED          UNSTRUCTURED
                                                                     UNSTRUCTURED
        DATA                  DATA                    DATA                  DATA
                                                                         DATA


     •   80% of world’s data is unstructured       •   90% of Kaiser’s data is
         (Rise of Mobility devices, and machine        unstructured (80% of EHR and Image
         generated data)                               data)

     •   44x as much data over the coming          •   25x as much data over the coming
         decade (35 zettabytes by 2020)                decade (One exabyte by 2020)

     •   Majority of data growth is driven by      •   Majority of data growth is driven by
         unstructured data (Active archives,           unstructured data (Medical Images,
         Medical images, Online movies and             Videos, Text, Voice)
         storage, Pictures)
                                                   •    Information is centric to providing
     •   Information is centric to new wave            Real-time Personalized Healthcare
         of opportunities (Retail, Financing,          (Requires Contextual – device,
         Insurance, Manufacturing, Healthcare,…)       environment, spatial, Demographics,
                                                       Social and Behavioral profiles in
                                                       addition to medical information)
     •   Industry is employing Big Data
         Technologies for Information              •   Kaiser is evaluating Big Data
         extraction                                    Technologies…

31       Source: Kaiser
Data Platform Compute Trends –
     Distributed Compute
                                                  Kaiser is looking to exploit
                                                  this capability…
      • Structured, Relational                                            • Unstructured, Non-tabular
        Tabular Data                                                        Data
      • Interactive Query Support                                         • Rich Ad Hoc Integration
                                                               Slave(s)
      • Real-time Analytics                                               • Real-time Analytics
      • SQL Transaction Data                                              • UQL ALL Data
                                                      Master        DAS



                                                                      Share-Nothing
                                                                 Distributed Storage and
                                  SAN/NAS                              Compute ($)
                                                  •     Fault-tolerant MasterSlave Architecture
                                     In-Memory          capable of withstanding partial system failures
                                        (50$)
                SAN/NAS                           •     Data is distributed across processing slave
                                                        nodes
                  MPP (10$)
                                                  •     Resources containing data are not shared

                                                  •     Master manages the data distribution, job
                                                        scheduling across slave nodes and aggregating
                SAN/NAS                                 result sets

                   SMP (Disk Caching,             •     Integrate built/bought Real-time Predictive
 SAN/NAS
                   High Speed Network)                  Analytical Solutions or Processing logic
                          (10$)
     SMP (5$)
                                         Discontinuous Change
32
Big Data Platform – Requirements
     Data                                                                    Process
 Characteristics                                                          Characteristics
                     Information drives process optimizations with
                      strategic impact. Modeling business intuition      Intuition
                                    from data deluge.                   (Simulation,
  Volume                                                               Optimization,
                                                                        Stochastic Optimization)
(Sensors, EMR,       Ability to model information and transition from
Claims, Pharmacy,
Images)
                     multiple access methods to generating, sharing,     Information
                     collaborating and acting on insights anytime,      (Standard & Ad Hoc
                                  anywhere on any device.               reporting, Query, Alerts,
                                                                        Forecasting, Access)
 Velocity 
                       Support current BI tools focused on structured
(SLAs, Real-time     information. Build/buy packaged unstructured        Interrogation
Decision Support &         data processing and analytics tools.         (Clustering, Statistical,
Contextual                                                              Quality, Semantics)
Intelligence)

                        A portfolio of tools to manage (profile,
                        cleanse, classify, synchronize, aggregate,
                                                                         Integration
                                                                        (Alignment, Semantics,
                           integrate, share) ALL types of data.         Completeness, Quality)
  Variety 
(Structured, Text,
Unstructured,         A unified information storage methodology          Ingestion
Documents, Images)
                     enabling users to manage data from ALL sources.    (Data Model, Metadata
                                                                        Reference Data, Store)



33
Big Data – Selection Criteria
       DATA SIZE     Gigabytes, Terabytes,   Petabytes
       DATA TYPE     Structured, Semi-Structured,   Unstructured
      DATA CLASS     Human Generated,     Machine Generated
     DATA CATALOG    Text, Image, Audio, Video
     DATA VELOCITY   Batch, Streaming
      DATA ACCESS    Analytics, Search, Transaction (ACID, BASE)
     DATABASE TYPE   Relational ,   File Based, Columnar, NoSQL, Document, Graph, RDF
        SERVER
     ARCHITECTURE    SMP, MMP,    Distributed Processing
      DISTRIBUTED
      PROCESSING     Appliance,   Commodity Cluster (CC) < 1K nodes, CC >1K nodes

       STORAGE
     ARCHITECTURE    NAS, SAN,      Direct Access Storage, Spinning Disks, Flash, SSD

                     Financial, Computer Vision Engine, Geospatial, Machine Learning,
      FRAMEWORKS
                     Mathematical, Natural Language Processing, Neural Networks,
                     Statistical Modeling, Time-Series         Analysis, Voice Engine
       ANALYTICS     Standard Reporting, Ad hoc Reporting, Query/Drill downs,   Alerts
                     Forecasting, Simulations, Optimization,          Stochastic Optimizations
34
Agenda

     • Healthcare and Big Data Trends
     • What is Big Data in Healthcare?
     • Big Data Challenges
     • Methods to Manage Big Data
     • Use Cases
     • Summary and Next Steps




35
Summary
     • We are at an inflection point
       in Big Data and analytics in
       healthcare
     • We need to make Big Data
       efficient and accessible
     • Focus on innovation, rely on
       the ecosystem for services
       outside your core competency
     • Adopt standards and best
       practices leveraging
       worldwide models




36
Next Steps
     Help build the Big Data Health Continuum:
     •   Create technology-differentiated offerings,
         advocating open standards and best practices
     •   Identify potential customers and ecosystem
         partners in core healthcare usage models
     •   Deliver industry proof points to accelerate
         adoption
     •   Develop joint marketing programs to raise
         awareness, amplify our thought leadership and
         generate customer value



           Together, We Create the Network Effect

37
37
Additional Sources of Information
     •   Big Data and Analytics at Intel - Intel® Big Data and Analytics
     •   Healthcare Blogs – Intel Healthcare IT Professionals
     •   Whitepapers
         – The Growing Importance of Big Data, Real Time Analytics
         – SAP In-Memory Appliance Software: Real-Time Business
           Intelligence
         – Oracle: Big Data for Enterprise
         – Big Data: The next frontier for innovation, competition, and
           productivity

     •   Videos
         – SAP-HANA – A Collaboration Between SAP & Intel




38
Intel Technologies
     • Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilization
       by consolidating multiple environments into a single server, workstation, or PC
     • Intel® vPro™ Technology – Designed specifically for the needs of business, notebooks and desktops
       with Intel® vPro™ technology have security and manageability built right into the chip
     • Intel® Trusted Execution Technology (Intel® TXT) – Protect confidentiality and integrity of
       business data against software-based attacks.
     • Intel® Anti-Theft Technology (Intel® AT) – Providing the option to activate hardware-based client-
       side intelligence to secure the PC and its data in the event the notebook is lost or stolen
     • Intel® AES New Instructions (Intel® AES-NI) – The Advanced Encryption Standard (AES)
       algorithm is now widely used across the software ecosystem to protect network traffic, personal data,
       and corporate IT infrastructures
     • Intel® Identity Protection Technology (Intel® IPT) – Two-factor authentication directly into the
       processors of select 2nd generation Intel® Core™ processor-based PCs
     • Intel® Cloud Access 360 – Protection Enterprise Access to Cloud and Protecting Enterprise
       Applications in the Cloud
     • Intel® Expressway Service Gateway – High performance security, xml acceleration and routing.
       Cross-domain service mediation, threat prevention, policy enforcement. Interoperable ESB gateway
     • McAfee Cloud Security Platform* – Consistent security policies, reporting, and threat intelligence
       across all cloud traffic—now available from a single platform
     • Intel® Scale-out Storage – Tackle your data center’s challenges with enterprise storage solutions
       powered by the world’s most advanced multi-core architecture
     • Intel® Solid State Drives – High performance, Self-Encrypting Solid State Drives for protecting
       sensitive data at rest
     • Intel Unified Networking – Unified Networking enables cost-effective connectivity to the LAN and
       the SAN on the same Ethernet fabric


39
Legal Disclaimer
INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED,
BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS
PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER
AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING
LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY
PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT.
• A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in
  personal injury or death. SHOULD YOU PURCHASE OR USE INTEL'S PRODUCTS FOR ANY SUCH MISSION CRITICAL
  APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND
  THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES
  AND REASONABLE ATTORNEYS' FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY,
  PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL
  OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF
  ITS PARTS.
• Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the
  absence or characteristics of any features or instructions marked "reserved" or "undefined". Intel reserves these for future
  definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The
  information here is subject to change without notice. Do not finalize a design with this information.
• The products described in this document may contain design defects or errors known as errata which may cause the product to
  deviate from published specifications. Current characterized errata are available on request.
• Intel product plans in this presentation do not constitute Intel plan of record product roadmaps. Please contact your Intel
  representative to obtain Intel's current plan of record product roadmaps.
• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor
  family, not across different processor families. Go to: http://www.intel.com/products/processor_number.
• Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order.
• Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be
  obtained by calling 1-800-548-4725, or go to: http://www.intel.com/design/literature.htm
• Intel, Xeon, Core, Phi, vPro, Sponsors of Tomorrow and the Intel logo are trademarks of Intel Corporation in the United States
   and other countries.

• *Other names and brands may be claimed as the property of others.
• Copyright ©2013 Intel Corporation.




40
Legal Disclaimer
     •   Intel® vPro™ Technology is sophisticated and requires setup and activation. Availability of features and results will
         depend upon the setup and configuration of your hardware, software and IT environment. To learn more
         visit: http://www.intel.com/technology/vpro.
     •   Ultrabook Touch/Convertibility: Ultrabook™ products are offered in multiple models. Some models may not be available in
         your market. Consult your Ultrabook™ manufacturer. For more information and details, visit
         http://www.intel.com/ultrabook .
     •   Intel® Virtualization Technology (Intel® VT) requires a computer system with an enabled Intel® processor, BIOS, and
         virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and
         software configurations. Software applications may not be compatible with all operating systems. Consult your PC
         manufacturer. For more information, visit http://www.intel.com/go/virtualization.
     •   Intel® AES-NI requires a computer system with an AES-NI enabled processor, as well as non-Intel software to execute the
         instructions in the correct sequence. AES-NI is available on select Intel® processors. For availability, consult your reseller
         or system manufacturer. For more information, see Intel® Advanced Encryption Standard Instructions (AES-NI)
     •   Intel® Active Management Technology (Intel® AMT) requires activation and a system with a corporate network
         connection, an Intel® AMT-enabled chipset, network hardware and software. For notebooks, Intel AMT may be unavailable
         or limited over a host OS-based VPN, when connecting wirelessly, on battery power, sleeping, hibernating or powered off.
         Results dependent upon hardware, setup and configuration. For more information, visit Intel® Active Management
         Technology.
     •   Intel® Anti-Theft Technology (Intel® AT): No system can provide absolute security under all conditions. Requires an
         enabled chipset, BIOS, firmware and software, and a subscription with a capable Service Provider. Consult your system
         manufacturer and Service Provider for availability and functionality. Intel assumes no liability for lost or stolen data and/or
         systems or any other damages resulting thereof. For more information, visit http://www.intel.com/go/anti-theft.
     •   Intel® Trusted Execution Technology (Intel® TXT): No computer system can provide absolute security under all
         conditions. Intel® TXT requires a computer with Intel® Virtualization Technology, an Intel TXT enabled processor,
         chipset, BIOS, Authenticated Code Modules and an Intel TXT compatible measured launched environment (MLE). Intel TXT
         also requires the system to contain a TPM v1.s. For more information, visit http://www.intel.com/technology/security.
     •   Intel® Identity Protection Technology (Intel® IPT): No system can provide absolute security under all
         conditions. Requires an Intel® Identity Protection Technology-enabled system, including a 2nd Generation Intel® Core™
         processor enabled chipset, firmware and software, and participating website. Consult your system manufacturer. Intel
         assumes no liability for lost or stolen data and/or systems or any resulting damages. For more information, visit
         http://ipt.intel.com.




41
Risk Factors
 The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the
 future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,”
 “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking
 statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking
 statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors
 could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the
 following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand
 could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance
 of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns
 including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial
 conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could
 negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by
 a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult
 to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and
 market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing
 programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological
 developments and to incorporate new features into its products. The gross margin percentage could vary significantly from
 expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying
 products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and
 associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials
 or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and
 intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in
 countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters,
 infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and
 compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's
 products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures.
 Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The
 succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In
 connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are
 being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's
 results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and
 by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as
 the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an
 injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting
 Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed
 discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most
 recent Form 10-Q, report on Form 10-K and earnings release.
     Rev. 1/17/13


42

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Big Data Solutions for Healthcare

  • 1. Big Data Solutions for Healthcare Wayne Wu, Global Health Solution Architect, Intel Hubert Ding, Healthcare Solution Architect, Intel BIGS001
  • 2. 2
  • 3. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ URL is on top of Session Agenda Pages in Pocket Guide 3
  • 4. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 4
  • 5. We are at an Inflection Point in Healthcare - TRENDS % of population over age 60 30+ % 25-29% 20-24% 2050 10-19% 0-9% WW Average Age 60+: 21% Source: United Nations “Population Aging 2002” Healthcare costs are Global AGING U.S. Healthcare BIG DATA RISING Average age 60+: Value Significant % of GDP growing from 10% to $300 Billion in value/year 21% by 2050 ~ 0.7% annual productivity growth Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast 5
  • 6. We are at an Inflection Point in Healthcare - TRENDS Storage Growth Total Data Healthcare Providers (PB) 15000 Admin Imaging Medical Imaging Archive Projection 10000 Case from just 1 healthcare system EMR Email 5000 File Non Clin Img 0 Research 2010 2011 2012 2013 2014 2015 Data Explosion projected to reach 35 Zetabytes by 2020, with a 44-fold increase from 2009 Source: McKinsey Global Institute Analysis ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast 6
  • 7. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 7
  • 8. Big Data in Healthcare Where is the data coming from? 2. Clinical Decision Support & 1. Pharma/Life Sciences Trends (includes Diagnostic Imaging) 4. Patient Behavior/Social 3. Claims, Utilization and Fraud Networking How do we create value? (examples) 1. Personalized Medicine 2. Clinical Decision Support 4. Analytics for Lifestyle and 3. Enhanced Fraud Detection Behavior-induced Diseases McKinsey Global Institute Analysis 8
  • 9. Big Data Solution for Healthcare Health Info Personal Health Primary Care Aging Society Services Management New Healthcare Clinical Decision Personalized Applications Cancer Genomics Support Medicine Analytics and Medical Imaging SQL-like Query Machine Learning Visualization Analytics Data Processing/ Medical Records Genome Data Medical Images Management Distributed Storage Security and Imaging Platform Optimization Privacy Acceleration 9
  • 10. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 10
  • 11. Big Data Challenges are More than Data Size... And Require New Technologies Lab results, billing data, images, sensors data from Volume devices, genomics • Structured data in standard formats like HL7 Variety • Unstructured data from dictations, transcription, photos, images Analyzing data from existing databases for claims, Value patient history, archived images, real-time data analytics for clinical decision support • Realtime rather than batch-style analysis Velocity • Data streamed in, tortured, and discarded • Making impact on the spot rather than after-the-fact Traditional business solutions connecting to new data and analytics models for real-time value opportunities 11
  • 12. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 12
  • 13. All Eyes on Data for Value Big Data Storage Considerations Traditional Solution Traditional Storage Approaches Environments Large Analytics – Hadoop* ERP, CRM, Batch, Large DB – Hive* OLTP-DB Large Backup – Lustre* Edge Servers Analytical Synchronization End-to-End Machine-to-Machine Source-to-Source Data Source Text-Voice-Video-Sensor Requesting Or M2M Communication Batch – Business Applications Rich Visualization – Secure Data Analysis and Caching 13
  • 14. All Eyes on Data for Value Data Center Provisioning Discrete Virtual Cloud – As A Service Big Data Storage Considerations HPC Traditional Solution Traditional Storage Approaches Environments Large Analytics – Hadoop* ERP, CRM, Batch, Large DB – Hive* OLTP-DB Large Backup – Lustre* Edge Servers Analytical Synchronization End-to-End Machine-to-Machine Source-to-Source Operational Solution Stack Example Applications & Services Data Source Text-Voice-Video-Sensor Visualization – File Structure & Analytical Requesting Or M2M Tools Communication Data Delivery, Operational & Graphical Batch – Business Applications Analytics Data Management & Computational Analytics Rich Visualization – Secure Data Analysis and Caching Compute – Storage & Infrastructure Platforms 14
  • 15. Enterprise Big Data Architecture STRUCTURED ENTERPRISE DATA PLATFORMS DATA PLATFORMS TOOLS Legacy Node Node Node ODS & Data Marts Data Mining Hadoop* Dev IDE Logs Create Map CONSUME REDUCE IMPORT Enterprise Data Warehouse UNSTRUCTURED Visualize Social & Web No-SQL Spreadsheets INSIGHTS Legacy APPS Document In Memory DB Types SQL Web Transcriptions & Apps Notes RDBMS MashUps 15
  • 16. Big Data Architectural Framework Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 16
  • 17. Big Data Architectural Framework Data as a Services Distributed Virtual Persistence HPC / TCP Vertically Event, Message Real-Time, Cached, Federated EDW, Marts MIC Integrated Data Software Characteristics Intel Data ingestion, Integration and Processing Services AIM Data Volume Suite and Distributed High Performance Integration Data Processing Quality Tools Hadoop* MapReduce Cloud Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 17
  • 18. Big Data Architectural Framework Data Access Data User Visibility Authentication NLP/Semantic Custom Analytic Solutions BI & Predictive Analytics Search/ Integrated Machine MapReduce Textual Analytics Analytics with Existing BI/Analytics Learning Hadoop with in-database Knowledge Streaming Analytics Support data processing support Management Data as a Services Distributed Virtual Persistence HPC / TCP Vertically Event, Message Real-Time, Cached, Federated EDW, Marts MIC Integrated Data Software Characteristics Intel Data ingestion, Integration and Processing Services AIM Data Suite Volume Distributed High Performance Integration Data Processing and Tools Quality Hadoop* MapReduce Cloud Data Provisioning Models-Storage & Connectivity Considerations Velocity NAS - SAS and Databases 10GBe MPP Databases Data Distributed DBMS / NoSQL Fast Fabric DW Appliances Vulnerability Storage Human Data Sources Security Genome Surveillance and Text, Video GIS Services Diagnostic Medical Social Medical & Drug Images Medical Device Log and Audio Privacy Devices Streaming Data Media Records Discovery Files Compliance Provisioning Models Can Vary by Data Characteristics 18
  • 19. Accessing Big Data (Clients) “Know Me” “Free Me” “Express Me” “Link Me” Mobile Laptops, Smart Clinical Tablet Ultrabook™ Fixed Digital Phone Assistant PCs Devices PCs Signage Kiosk Mobility Vital sign, I & O entry Medication administration Template data entry Free-format text data entry Large diagnostic images Data inquiry Manageability 19
  • 20. Building on the Ecosystem Database and Analytics Environments Optimized on Intel Life Sciences Database and compute infrastructure Analytics engines Workloads & Solutions Relational VOLTDB EXALYTICS Nonrelational Open Source: BLAST, FASTA, ClustalW, HMMER, Darwin, etc. No Matter the Choice: All optimized on Intel® Xeon® processor based hardware 20
  • 21. Intel® Products and Software For Big Data Scaling Compute Flexible Workloads & Intel® Xeon® processor E5- and E7 based servers up to Analysis Technical Compute 80% performance boost with Optimized Intel Xeon processor E5- hardware-enhanced security Data Delivery & based servers for TCP Intel® Xeon Phi™ co- Storage Management processor Intelligent scale-out storage Software Ecosystem Integrated Systems built with Intel Xeon Interconnect Embedded Analysis Solutions processor E5-based storage From Intel ISG Efficiency Robust & Secure Interconnect Visibly Mobile Intel Software EcoSystem Fast Fabric & Caching Performance Client Hadoop* Investing in new fabric Lustre* approaches Rich Visualization In-memory non-volatile memory that Seamless Access In stream data analysis provide capacity caching for End to end security data velocity Performance Client Network Rich modeling support Intelligent scale-out Client – server application networking management from 10GBe – 40GBe 21
  • 22. Examples of Intel-powered Servers in Big Data and Analytics Cisco* UCS Server1 Dell* PowerEdge* C Series2 Oracle* Sun Fire* server3 Intel® Xeon® Intel Xeon processor Intel Xeon processor E7- processor 5600 5500/5600 4800 Cisco UCS server with EMC The Dell | Cloudera* solution Oracle Exalytics* In-Memory Greenplum MR software - for Apache* Hadoop combines Machine, features the Oracle “enterprise-class” BI Foundation Suite and Hadoop* distribution that Oracle TimesTen In-Memory features technology from Database for Exalytics MapR Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon® processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase. 1 http://gigaom.com/cloud/ciscos-servers-now-tuned-for-hadoop/ 2 http://www.businesswire.com/news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data 3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine 22
  • 23. Big Data Applications in Healthcare (PRC) 2. Clinical •药品研发 Decision •临床数据比对 对药品实际 作用进行分析;实 1. Pharma/Life Support & 匹配同类型的病人,用药 施药品市场预测 Sciences Trends •临床决策支持 •基因测序 利用规则和数据实时分析给 •分布式计算加快基因测序计算 (includes 出智能提示 效率 Diagnostic Imaging) •远程监控 •公共卫生实时统计分析 采集并分析病人随身携带仪 发现公共卫生疫情及公民健康 4. Patient 器数据,给出智能建议 状况 3. Claims, Behavior/ •人口统计学分析 •新农合基金数据分析 Utilization & Social 对不同群体人群的就医,健 及时了解基金状况,预测风险 Fraud 康数据实施人口统计分析 辅助制定农合基金的起付线, Networking 赔付病种等 •了解病人就诊行为 发现病人的特定就诊行为, •基本药物临床应用分析 分配医疗资源 分析基本药物在处方中的比例 23
  • 24. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 24
  • 25. Use Case: Regional Health Info Network – China Real-time Clinical Decision Support Data Analytic Care Coordination • Real-time and recursive information R&D … Clinical decision support … processing of health data (EHR, medical images) to support care RHIN coordination, clinical decision Ancillary Health EHR Registries support, and public health Data & Services Information DW Data & Services Data & Services management • Enabling health data analytic with Longitudinal Record Services Hadoop* (HBase*/Hive*) • Potential to scale cross geos and Health Information Access Layer across sectors/segments Primary care Public • Involving local ISVs, local OEMs Hospital (Grassroots) Health • Technical Challenges – Transforming the relational DB to Hadoop (HBase/Hive) – Addressing the usages of big data analytics in RHIN 25
  • 26. RHIN/Grassroots Solution with Big Data (Hadoop*) Integrated User Interface(Citizen, Physician, Health Authority) Cloud -based Regional Healthcare Service Distributed Data Service System System Multi-Tenancy Application Presentation (Report, Viewer) Pubic Medical Health Service New Rural Data Mining (Mahout*) Language & Compiler (Hive*) Medical Distributed Batch Processing Real-time Insurance Framework Database Operation Drug Mgt. (MapReduce) (HBase*) Mgt. Service Distributed File System Coordination Service (HDFS) (ZooKeeper*) Infrastructure Virtualization Structured Data Collector Log Date Collector (Sqoop*) (Flume*) Network Storage Server Virtualization Virtualization Virtualization EHR data Repository Health Information Access Layer (HIAL) Grassroots Hospital Hospital Care 26 Institution
  • 27. Use Case: NEXTBIO Analytics for Genomics Data • Cost to sequence a genome has fallen by 800x in the last 4 years • Each genome has ~4 million variants • Growth in the genomics data in the public and private domain • Data available in variety of sources – Structured, semi-structured, unstructured • New aggregated data growing exponentially Data Interpretation & Commercializing Sequencing Processing Analytics Targeted Cloud Storage Therapeutics Visualization Companion 3 Billion Base Pairs Millions of Millions of Variants Diagnostics Variants Millions of Patients Actionable Biomarkers 27
  • 28. Use Case: NEXTBIO Patient Correlation Data Novel Discoveries Biomarkers Disease Mechanism Drug Indications Clinical Trial Parameters Patient Care Options Large content repository of public and private genomic data combined with proprietary and patented correlation engine 28
  • 29. Use Case: NEXTBIO Nextbio & Intel Collaboration Technical Challenge: • Immutable Data – write once, never change, read many times • Traditional Bloom Filters works • Hadoop* & HBase* well suited 1 genome  10 million rows 100 genomes  1billion rows 1M genomes  10 trillion rows 100M genomes  1 quadrillion 1,000,000,000,000,000 rows • App can dynamically partitions HBase as data size grows Intel Optimizations for Hadoop: • Optimized Hadoop stack in open source • Stabilize HBase to provide reliable scalable deployment • Optimize and support scale-out as data size dramatically grows • Exploring cluster auto tuning, Security & Compliance, etc. 29
  • 30. Use Case: Big Data at Kaiser Permanente 30
  • 31. Data Trends World’s Data Kaiser’s Data 90% 80% STRUCTURED UNSTRUCTURED STRUCTURED UNSTRUCTURED UNSTRUCTURED DATA DATA DATA DATA DATA • 80% of world’s data is unstructured • 90% of Kaiser’s data is (Rise of Mobility devices, and machine unstructured (80% of EHR and Image generated data) data) • 44x as much data over the coming • 25x as much data over the coming decade (35 zettabytes by 2020) decade (One exabyte by 2020) • Majority of data growth is driven by • Majority of data growth is driven by unstructured data (Active archives, unstructured data (Medical Images, Medical images, Online movies and Videos, Text, Voice) storage, Pictures) • Information is centric to providing • Information is centric to new wave Real-time Personalized Healthcare of opportunities (Retail, Financing, (Requires Contextual – device, Insurance, Manufacturing, Healthcare,…) environment, spatial, Demographics, Social and Behavioral profiles in addition to medical information) • Industry is employing Big Data Technologies for Information • Kaiser is evaluating Big Data extraction Technologies… 31 Source: Kaiser
  • 32. Data Platform Compute Trends – Distributed Compute Kaiser is looking to exploit this capability… • Structured, Relational • Unstructured, Non-tabular Tabular Data Data • Interactive Query Support • Rich Ad Hoc Integration Slave(s) • Real-time Analytics • Real-time Analytics • SQL Transaction Data • UQL ALL Data Master DAS Share-Nothing Distributed Storage and SAN/NAS Compute ($) • Fault-tolerant MasterSlave Architecture In-Memory capable of withstanding partial system failures (50$) SAN/NAS • Data is distributed across processing slave nodes MPP (10$) • Resources containing data are not shared • Master manages the data distribution, job scheduling across slave nodes and aggregating SAN/NAS result sets SMP (Disk Caching, • Integrate built/bought Real-time Predictive SAN/NAS High Speed Network) Analytical Solutions or Processing logic (10$) SMP (5$) Discontinuous Change 32
  • 33. Big Data Platform – Requirements Data Process Characteristics Characteristics Information drives process optimizations with strategic impact. Modeling business intuition  Intuition from data deluge. (Simulation, Volume  Optimization, Stochastic Optimization) (Sensors, EMR, Ability to model information and transition from Claims, Pharmacy, Images) multiple access methods to generating, sharing,  Information collaborating and acting on insights anytime, (Standard & Ad Hoc anywhere on any device. reporting, Query, Alerts, Forecasting, Access) Velocity  Support current BI tools focused on structured (SLAs, Real-time information. Build/buy packaged unstructured  Interrogation Decision Support & data processing and analytics tools. (Clustering, Statistical, Contextual Quality, Semantics) Intelligence) A portfolio of tools to manage (profile, cleanse, classify, synchronize, aggregate,  Integration (Alignment, Semantics, integrate, share) ALL types of data. Completeness, Quality) Variety  (Structured, Text, Unstructured, A unified information storage methodology  Ingestion Documents, Images) enabling users to manage data from ALL sources. (Data Model, Metadata Reference Data, Store) 33
  • 34. Big Data – Selection Criteria DATA SIZE Gigabytes, Terabytes, Petabytes DATA TYPE Structured, Semi-Structured, Unstructured DATA CLASS Human Generated, Machine Generated DATA CATALOG Text, Image, Audio, Video DATA VELOCITY Batch, Streaming DATA ACCESS Analytics, Search, Transaction (ACID, BASE) DATABASE TYPE Relational , File Based, Columnar, NoSQL, Document, Graph, RDF SERVER ARCHITECTURE SMP, MMP, Distributed Processing DISTRIBUTED PROCESSING Appliance, Commodity Cluster (CC) < 1K nodes, CC >1K nodes STORAGE ARCHITECTURE NAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSD Financial, Computer Vision Engine, Geospatial, Machine Learning, FRAMEWORKS Mathematical, Natural Language Processing, Neural Networks, Statistical Modeling, Time-Series Analysis, Voice Engine ANALYTICS Standard Reporting, Ad hoc Reporting, Query/Drill downs, Alerts Forecasting, Simulations, Optimization, Stochastic Optimizations 34
  • 35. Agenda • Healthcare and Big Data Trends • What is Big Data in Healthcare? • Big Data Challenges • Methods to Manage Big Data • Use Cases • Summary and Next Steps 35
  • 36. Summary • We are at an inflection point in Big Data and analytics in healthcare • We need to make Big Data efficient and accessible • Focus on innovation, rely on the ecosystem for services outside your core competency • Adopt standards and best practices leveraging worldwide models 36
  • 37. Next Steps Help build the Big Data Health Continuum: • Create technology-differentiated offerings, advocating open standards and best practices • Identify potential customers and ecosystem partners in core healthcare usage models • Deliver industry proof points to accelerate adoption • Develop joint marketing programs to raise awareness, amplify our thought leadership and generate customer value Together, We Create the Network Effect 37 37
  • 38. Additional Sources of Information • Big Data and Analytics at Intel - Intel® Big Data and Analytics • Healthcare Blogs – Intel Healthcare IT Professionals • Whitepapers – The Growing Importance of Big Data, Real Time Analytics – SAP In-Memory Appliance Software: Real-Time Business Intelligence – Oracle: Big Data for Enterprise – Big Data: The next frontier for innovation, competition, and productivity • Videos – SAP-HANA – A Collaboration Between SAP & Intel 38
  • 39. Intel Technologies • Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilization by consolidating multiple environments into a single server, workstation, or PC • Intel® vPro™ Technology – Designed specifically for the needs of business, notebooks and desktops with Intel® vPro™ technology have security and manageability built right into the chip • Intel® Trusted Execution Technology (Intel® TXT) – Protect confidentiality and integrity of business data against software-based attacks. • Intel® Anti-Theft Technology (Intel® AT) – Providing the option to activate hardware-based client- side intelligence to secure the PC and its data in the event the notebook is lost or stolen • Intel® AES New Instructions (Intel® AES-NI) – The Advanced Encryption Standard (AES) algorithm is now widely used across the software ecosystem to protect network traffic, personal data, and corporate IT infrastructures • Intel® Identity Protection Technology (Intel® IPT) – Two-factor authentication directly into the processors of select 2nd generation Intel® Core™ processor-based PCs • Intel® Cloud Access 360 – Protection Enterprise Access to Cloud and Protecting Enterprise Applications in the Cloud • Intel® Expressway Service Gateway – High performance security, xml acceleration and routing. Cross-domain service mediation, threat prevention, policy enforcement. Interoperable ESB gateway • McAfee Cloud Security Platform* – Consistent security policies, reporting, and threat intelligence across all cloud traffic—now available from a single platform • Intel® Scale-out Storage – Tackle your data center’s challenges with enterprise storage solutions powered by the world’s most advanced multi-core architecture • Intel® Solid State Drives – High performance, Self-Encrypting Solid State Drives for protecting sensitive data at rest • Intel Unified Networking – Unified Networking enables cost-effective connectivity to the LAN and the SAN on the same Ethernet fabric 39
  • 40. Legal Disclaimer INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. • A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. SHOULD YOU PURCHASE OR USE INTEL'S PRODUCTS FOR ANY SUCH MISSION CRITICAL APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES AND REASONABLE ATTORNEYS' FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY, PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF ITS PARTS. • Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined". Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information. • The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. • Intel product plans in this presentation do not constitute Intel plan of record product roadmaps. Please contact your Intel representative to obtain Intel's current plan of record product roadmaps. • Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. Go to: http://www.intel.com/products/processor_number. • Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order. • Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be obtained by calling 1-800-548-4725, or go to: http://www.intel.com/design/literature.htm • Intel, Xeon, Core, Phi, vPro, Sponsors of Tomorrow and the Intel logo are trademarks of Intel Corporation in the United States and other countries. • *Other names and brands may be claimed as the property of others. • Copyright ©2013 Intel Corporation. 40
  • 41. Legal Disclaimer • Intel® vPro™ Technology is sophisticated and requires setup and activation. Availability of features and results will depend upon the setup and configuration of your hardware, software and IT environment. To learn more visit: http://www.intel.com/technology/vpro. • Ultrabook Touch/Convertibility: Ultrabook™ products are offered in multiple models. Some models may not be available in your market. Consult your Ultrabook™ manufacturer. For more information and details, visit http://www.intel.com/ultrabook . • Intel® Virtualization Technology (Intel® VT) requires a computer system with an enabled Intel® processor, BIOS, and virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and software configurations. Software applications may not be compatible with all operating systems. Consult your PC manufacturer. For more information, visit http://www.intel.com/go/virtualization. • Intel® AES-NI requires a computer system with an AES-NI enabled processor, as well as non-Intel software to execute the instructions in the correct sequence. AES-NI is available on select Intel® processors. For availability, consult your reseller or system manufacturer. For more information, see Intel® Advanced Encryption Standard Instructions (AES-NI) • Intel® Active Management Technology (Intel® AMT) requires activation and a system with a corporate network connection, an Intel® AMT-enabled chipset, network hardware and software. For notebooks, Intel AMT may be unavailable or limited over a host OS-based VPN, when connecting wirelessly, on battery power, sleeping, hibernating or powered off. Results dependent upon hardware, setup and configuration. For more information, visit Intel® Active Management Technology. • Intel® Anti-Theft Technology (Intel® AT): No system can provide absolute security under all conditions. Requires an enabled chipset, BIOS, firmware and software, and a subscription with a capable Service Provider. Consult your system manufacturer and Service Provider for availability and functionality. Intel assumes no liability for lost or stolen data and/or systems or any other damages resulting thereof. For more information, visit http://www.intel.com/go/anti-theft. • Intel® Trusted Execution Technology (Intel® TXT): No computer system can provide absolute security under all conditions. Intel® TXT requires a computer with Intel® Virtualization Technology, an Intel TXT enabled processor, chipset, BIOS, Authenticated Code Modules and an Intel TXT compatible measured launched environment (MLE). Intel TXT also requires the system to contain a TPM v1.s. For more information, visit http://www.intel.com/technology/security. • Intel® Identity Protection Technology (Intel® IPT): No system can provide absolute security under all conditions. Requires an Intel® Identity Protection Technology-enabled system, including a 2nd Generation Intel® Core™ processor enabled chipset, firmware and software, and participating website. Consult your system manufacturer. Intel assumes no liability for lost or stolen data and/or systems or any resulting damages. For more information, visit http://ipt.intel.com. 41
  • 42. Risk Factors The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological developments and to incorporate new features into its products. The gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures. Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent Form 10-Q, report on Form 10-K and earnings release. Rev. 1/17/13 42